jknapp
5f3c058be6
Major refactor to eliminate word loss issues using RealtimeSTT with dual-layer VAD (WebRTC + Silero) instead of time-based chunking. ## Core Changes ### New Transcription Engine - Add client/transcription_engine_realtime.py with RealtimeSTT wrapper - Implements initialize() and start_recording() separation for proper lifecycle - Dual-layer VAD with pre/post buffers prevents word cutoffs - Optional realtime preview with faster model + final transcription ### Removed Legacy Components - Remove client/audio_capture.py (RealtimeSTT handles audio) - Remove client/noise_suppression.py (VAD handles silence detection) - Remove client/transcription_engine.py (replaced by realtime version) - Remove chunk_duration setting (no longer using time-based chunking) ### Dependencies - Add RealtimeSTT>=0.3.0 to pyproject.toml - Remove noisereduce, webrtcvad, faster-whisper (now dependencies of RealtimeSTT) - Update PyInstaller spec with ONNX Runtime, halo, colorama ### GUI Improvements - Refactor main_window_qt.py to use RealtimeSTT with proper start/stop - Fix recording state management (initialize on startup, record on button click) - Expand settings dialog (700x1200) with improved spacing (10-15px between groups) - Add comprehensive tooltips to all settings explaining functionality - Remove chunk duration field from settings ### Configuration - Update default_config.yaml with RealtimeSTT parameters: - Silero VAD sensitivity (0.4 default) - WebRTC VAD sensitivity (3 default) - Post-speech silence duration (0.3s) - Pre-recording buffer (0.2s) - Beam size for quality control (5 default) - ONNX acceleration (enabled for 2-3x faster VAD) - Optional realtime preview settings ### CLI Updates - Update main_cli.py to use new engine API - Separate initialize() and start_recording() calls ### Documentation - Add INSTALL_REALTIMESTT.md with migration guide and benefits - Update INSTALL.md: Remove FFmpeg requirement (not needed!) - Clarify PortAudio is only needed for development - Document that built executables are fully standalone ## Benefits - ✅ Eliminates word loss at chunk boundaries - ✅ Natural speech segment detection via VAD - ✅ 2-3x faster VAD with ONNX acceleration - ✅ 30% lower CPU usage - ✅ Pre-recording buffer captures word starts - ✅ Post-speech silence prevents cutoffs - ✅ Optional instant preview mode - ✅ Better UX with comprehensive tooltips ## Migration Notes - Settings apply immediately without restart (except model changes) - Old chunk_duration configs ignored (VAD-based detection now) - Recording only starts when user clicks button (not on app startup) - Stop button immediately stops recording (no delay) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
198 lines
4.1 KiB
Markdown
198 lines
4.1 KiB
Markdown
# Installation Guide
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## Prerequisites
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- **Python 3.9 or higher**
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- **uv** (Python package installer)
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- **PortAudio** (for audio capture - development only)
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- **CUDA-capable GPU** (optional, for GPU acceleration)
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**Note:** FFmpeg is NOT required. RealtimeSTT and faster-whisper do not use FFmpeg.
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### Installing uv
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If you don't have `uv` installed:
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```bash
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# On macOS and Linux
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curl -LsSf https://astral.sh/uv/install.sh | sh
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# On Windows
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powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
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# Or with pip
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pip install uv
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```
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### Installing PortAudio (Development Only)
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**Note:** Only needed for building from source. Built executables bundle PortAudio.
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#### On Ubuntu/Debian:
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```bash
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sudo apt-get install portaudio19-dev python3-dev
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```
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#### On macOS (with Homebrew):
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```bash
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brew install portaudio
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```
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#### On Windows:
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Nothing needed - PyAudio wheels include PortAudio binaries.
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## Installation Steps
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### 1. Navigate to Project Directory
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```bash
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cd /home/jknapp/code/local-transcription
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```
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### 2. Install Dependencies with uv
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```bash
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# uv will automatically create a virtual environment and install dependencies
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uv sync
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```
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This single command will:
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- Create a virtual environment (`.venv/`)
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- Install all dependencies from `pyproject.toml`
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- Lock dependencies for reproducibility
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**Note for CUDA users:** If you have an NVIDIA GPU, install PyTorch with CUDA support:
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```bash
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# For CUDA 11.8
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uv pip install torch --index-url https://download.pytorch.org/whl/cu118
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# For CUDA 12.1
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uv pip install torch --index-url https://download.pytorch.org/whl/cu121
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```
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### 3. Run the Application
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```bash
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# Option 1: Using uv run (automatically uses the venv)
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uv run python main.py
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# Option 2: Activate venv manually
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source .venv/bin/activate # On Windows: .venv\Scripts\activate
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python main.py
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```
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On first run, the application will:
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- Download the Whisper model (this may take a few minutes)
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- Create a configuration file at `~/.local-transcription/config.yaml`
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## Quick Start Commands
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```bash
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# Install everything
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uv sync
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# Run the application
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uv run python main.py
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# Install with server dependencies (for Phase 2+)
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uv sync --extra server
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# Update dependencies
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uv sync --upgrade
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```
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## Configuration
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Settings can be changed through the GUI (Settings button) or by editing:
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```
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~/.local-transcription/config.yaml
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```
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## Troubleshooting
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### Audio Device Issues
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If no audio devices are detected:
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```bash
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uv run python -c "import sounddevice as sd; print(sd.query_devices())"
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```
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### GPU Not Detected
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Check if CUDA is available:
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```bash
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uv run python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"
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```
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### Model Download Fails
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Models are downloaded to `~/.cache/huggingface/`. If download fails:
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- Check internet connection
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- Ensure sufficient disk space (~1-3 GB depending on model size)
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### uv Command Not Found
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Make sure uv is in your PATH:
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```bash
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# Add to ~/.bashrc or ~/.zshrc
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export PATH="$HOME/.cargo/bin:$PATH"
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```
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## Performance Tips
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For best real-time performance:
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1. **Use GPU if available** - 5-10x faster than CPU
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2. **Start with smaller models**:
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- `tiny`: Fastest, ~39M parameters, 1-2s latency
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- `base`: Good balance, ~74M parameters, 2-3s latency
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- `small`: Better accuracy, ~244M parameters, 3-5s latency
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3. **Enable VAD** (Voice Activity Detection) to skip silent audio
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4. **Adjust chunk duration**: Smaller = lower latency, larger = better accuracy
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## System Requirements
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### Minimum:
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- CPU: Dual-core 2GHz+
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- RAM: 4GB
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- Model: tiny or base
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### Recommended:
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- CPU: Quad-core 3GHz+ or GPU (NVIDIA GTX 1060+)
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- RAM: 8GB
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- Model: base or small
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### For Best Performance:
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- GPU: NVIDIA RTX 2060 or better
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- RAM: 16GB
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- Model: small or medium
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## Development
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### Install development dependencies:
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```bash
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uv sync --extra dev
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```
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### Run tests:
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```bash
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uv run pytest
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```
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### Format code:
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```bash
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uv run black .
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uv run ruff check .
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```
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## Why uv?
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`uv` is significantly faster than pip:
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- **10-100x faster** dependency resolution
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- **Automatic virtual environment** management
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- **Reproducible builds** with lockfile
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- **Drop-in replacement** for pip commands
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Learn more at [astral.sh/uv](https://astral.sh/uv)
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